Job offer

Background in physics, applied mathematics, computing, or physical sciences is required. Previous coding experience and familiarity with high performance computing is an asset. Fluency in English is required., PhD or equivalent

Research Field Environmental science

Education Level PhD or equivalent

Research Field Environmental science

The successful candidate will join the Climate Physics team at ENS de Lyon (https://climatephysics-ensl.fr/), which currently consists of 5 permanent researchers, 7 PhD students and 2 postdoctoral researchers. We combine laboratory experiments, numerical simulations, artificial intelligence and field observations to address outstanding questions in physical oceanography, atmospheric sciences, physical limnology (focusing on polar and alpine environments) and geophysical fluid dynamics. We collaborate with several colleagues from the Physics Laboratory, who are experts in hydrodynamics/climate research (about 15 PIs) and/or machine learning (about 10 PIs). The Physics Laboratory is about 180-member strong and conducts world-leading research on a broad range of topics, including quantum technology, statistical physics, biophysics and climate physics. Roughly 30 new doctoral students and postdoctoral researchers join the Laboratory every year. Sea-level projections are derived using Earth-system models (ESM). The uncertainties in these projections are dominated by uncertainties and poor representation of the interactions between the Antarctic Ice Sheet (AIS) and the ocean. AIS flows from the continent into the ocean, forming floating ice shelves, whose base is melted by ocean heat. At the same time, the change in the ice-shelf geometry, induced by ocean-driven melting affects the rate of ice flow into the ocean, which in turn modifies rates of melting. Accurate representation of these coupled interactions requires a coupled ice-sheet-ocean model - a feature that is missing from most state-of-the-art ESMs. Instead, sea-level projections are typically derived using standalone ice-sheet models, forced by ESM outputs that do not include a coupled ice-sheet model. Although there are several ongoing efforts to include coupled ice-sheet-ocean interactions within ESMs, high resolutions required to resolve ice-shelf cavities make this approach relatively expensive, given the large number of ensemble members needed for projections. This project aims to explore the coupling of the ocean and ice-sheet model components via a machine learning emulator of ice-shelf cavity circulation.